Vision based driving agent for race car simulation environments
This addresses the problem of emergency control for autonomous vehicles, but it is incremental as it applies existing DRL methods to a specific simulation task.
The paper tackled autonomous driving at the tire grip limit using Deep Reinforcement Learning (PPO) in a simulated race car environment, achieving professional human lap times with only visual inputs.
In recent years, autonomous driving has become a popular field of study. As control at tire grip limit is essential during emergency situations, algorithms developed for racecars are useful for road cars too. This paper examines the use of Deep Reinforcement Learning (DRL) to solve the problem of grip limit driving in a simulated environment. Proximal Policy Optimization (PPO) method is used to train an agent to control the steering wheel and pedals of the vehicle, using only visual inputs to achieve professional human lap times. The paper outlines the formulation of the task of time optimal driving on a race track as a deep reinforcement learning problem, and explains the chosen observations, actions, and reward functions. The results demonstrate human-like learning and driving behavior that utilize maximum tire grip potential.